Authorization

DataFleets keeps private data useful, and useful data private, with federated learning and $4.5M seed

As you may already know, there’s a lot of data out there, and some of it could actually be pretty useful. But privacy and security considerations often put strict limitations on how it can be used or analyzed. DataFleets promises a new approach by which databases can be safely accessed and analyzed without the possibility of privacy breaches or abuse — and has raised a $4.5 million seed round to scale it up.
To work with data, you need to have access to it. If you’re a bank, that means transactions and accounts; if you’re a retailer, that means inventories and supply chains, and so on. There are lots of insights and actionable patterns buried in all that data, and it’s the job of data scientists and their ilk to draw them out.
But what if you can’t access the data? After all, there are many industries where it is not advised or even illegal to do so, such as in healthcare. You can’t exactly take a whole hospital’s medical records, give them to a data analysis firm, and say “sift through that and tell me if there’s anything good.” These, like many other data sets, are too private or sensitive to allow anyone unfettered access. The slightest mistake — let alone abuse — could have serious repercussions.
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